Analysis of blood vessels from retinal images has clinical significance since retinal blood vessels are the first ones that show the symptoms of several pathologies, such as arterial hypertension, arteriosclerosis, and other systemic vascular diseases.
In order to detect such symptoms and to perform diagnosis, it is typically required to perform a series of image processing tasks. Currently available retinal analysis systems require significant manual input from the medical personnel, are subjective, and analysis is time-consuming and prone to error. Accurate, objective, reliable, and automatic (or semi-automatic) retinal image medical systems are currently not available with the performance required for routine clinical use.
As an example, the computation of the retina arteriovenous ratio (AVR), that is, the relation between afferent and efferent blood vessels of the retinal vascular tree, is significant in order to diagnose diseases and evaluate their consequences. Due to the unavailability of commercial retinal image analysis systems with precise and robust estimation of the AVR metric, analysis of the AVR is usually computed by a tedious and time consuming manual process. This the results in more expensive, subjective, and ophthalmologist-dependent AVR computations. Similarly, other clinical parameters derived from retinal image analysis are tedious and their manual computation is subjective.
Automatic retinal image analysis is an important field in order to calculate a series of biomedical parameters obtained from relevant structures in the retina. Blood vessels, i.e., arteries and veins, are among these relevant structures as their segmentation and measurement help to spot the presence of pathologies such as arterial hypertension, diabetic retinopathy, and other pathologies.
Among the many challenges related to the automatic analysis of retinal blood vessels, one of the most challenging problems to overcome is the automatic classification of said vessels into artery or vein. This is an important task in medical analysis in the automatic calculation of the AVR ratio and other similar clinical parameters. Nowadays, all the advances regarding artery/vein classification offer solutions based on manual vessel labeling, which is a tedious task for medical experts prone to high intra-expert and inter-expert variability due to factors such as exhaustion, expertise or image quality. Automatic approaches, method, or systems are not currently commercially available or disclosed in the patent literature.